Many enterprise data processing systems rely on multi-node database servers to store and manage data. Such enterprise data processing systems typically follow a multi-tier model that has a multi-node database server in the first tier, one or more computers in the middle tier linked to the database server via a network, and one or more computers in the outer tier.
A multi-node database server comprises multiple database instances running on a group of interconnected nodes managing access to a database. The nodes in the group may be in the form of computers (e.g. work stations, personal computers) interconnected via a network. Alternatively, the nodes may be the nodes of a grid, where each node is interconnected on a rack. The grid may host multiple multi-node database servers.
Enterprises monitor the performance of a multi-node database server to manage performance provided to the clients and users of the multi-node database server. To perform this function, information describing the workload placed on the resources of the multi-node database server is needed. Enterprise data processing systems generate such workload information.
An approach used for reporting workload information is the physical model approach. Under the physical model approach, workload information is demarcated by computers and components of computers that host a multi-node database server. For example, workload information is demarcated by node, database instance, and user session on a node.
Unfortunately, information provided under the physical model approach limits how performance of the multi-node database servers may be analyzed and managed. This limitation stems from the fact that workload information is delineated in a way that does not correlate well with the way it is being used by applications and browsers. For example, a business data processing system includes a multi-node database server, an online order entry application, accessible via the Internet, and other applications. The applications are executed by clients of the multi-node database server. To ensure customer satisfaction, the performance of the online order entry application is deemed critical. Computer resources should be allocated to the online order entry application in favor of the other applications.
Under the physical model approach, only information specific to the performance realized by a node, instance, and user session is available. Information about the specific performance realized for the online order entry system and other applications cannot be identified and measured. It is possible that workload information indicates that the performance of a node or database instance is good while performance realized for work performed for the online order entry application is in fact bad. Similarly, it is possible that workload information indicates that the performance of a node, database instance, and the online order entry application is good while performance experienced by the other applications is bad.
Based on the foregoing, there is clearly a need for an approach for managing and measuring workload information in a way not limited to physical components of the individual computer resources, and in a way that more closely correlates with the way resources are being used.
Approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.